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                                       Details for article 4 of 14 found articles
 
 
  Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection
 
 
Title: Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection
Author: Dewan Md. Farid
Nouria Harbi
Mohammad Zahidur Rahman
Appeared in: International journal of network security & its applications
Paging: Volume 2 (2010) nr. 2 pages 12-25
Year: 2010
Contents: In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposedalgorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) andsignificant reduce false positives (FP) for different types of network intrusions using limited computational resources
Publisher: Academy & Industry Research Collaboration Center (AIRCC) (provided by DOAJ)
Source file: Elektronische Wetenschappelijke Tijdschriften
 
 

                             Details for article 4 of 14 found articles
 
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